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dc.contributor.authorHu, Yingyaoen
dc.contributor.authorShum, Matthewen
dc.contributor.authorTan, Weien
dc.date.accessioned2011-09-27T15:21:07Z-
dc.date.available2011-09-27T15:21:07Z-
dc.date.issued2010-
dc.identifier.urihttp://hdl.handle.net/10419/49872-
dc.description.abstractWe present a method for estimating Markov dynamic models with unobserved state variables which can be serially correlated over time. We focus on the case where all the model variables have discrete support. Our estimator is simple to compute because it is noniterative, and involves only elementary matrix manipulations. Our estimation method is nonparametric, in that no parametric assumptions on the distributions of the unobserved state variables or the laws of motions of the state variables are required. Monte Carlo simulations show that the estimator performs well in practice, and we illustrate its use with a dataset of doctors' prescription of pharmaceutical drugs.en
dc.language.isoengen
dc.publisher|aThe Johns Hopkins University, Department of Economics |cBaltimore, MDen
dc.relation.ispartofseries|aWorking Paper |x558en
dc.subject.ddc330en
dc.titleA simple estimator for dynamic models with serially correlated unobservables-
dc.typeWorking Paperen
dc.identifier.ppn635250888en
dc.rightshttp://www.econstor.eu/dspace/Nutzungsbedingungenen

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